Multi-Armed Bandits Based Task Selection of A Mobile Crowdsensing Worker

Abstract

As the popularity of mobile devices continues to increase, Mobile Crowdsensing (MC), a scalable and efficient data collection method, has received widespread attention. Although lots of effort has been devoted to studying the task assignment or worker recruitment in MC, most of them focus on how to maximize the profit from the perspective of the platform while ignoring rational individual workers' entitlement. We creatively start from the worker's perspective to find the task selection strategy to maximize the worker's profit. In this paper, the problem of unknown task selection is modeled as a Multi-Armed Bandit (MAB), on which three types of additional constraints are considered. The first constraint is the device budget. Workers choose and conduct tasks before it is exhausted. The second constraint is the personal preference regarding the traveling cost. The third constraint is the balance requirement of the MC platform, which has regulations on the tasks' execution rounds. In addition to the dilemma between exploration and exploitation in the classical MAB, we have to face the tradeoff between the reward and all the constraints above. To this end, we first adopt the epoch-style algorithm to reduce the number of switches between any two sensing tasks and further build new algorithms to deal with different constraints. The traveling cost and platform balance are involved in the task index computation as a penalty. We conduct extensive simulations based on real-world traces to verify the significant performance of our proposed algorithms.